Working Paper Series, Department of Economics, Uppsala University
Estimation of DSGE models: Maximum Likelihood vs. Bayesian methods
Abstract: DSGE models are typically estimated using Bayesian
methods, but a researcher may want to estimate a DSGE model with full
information maximum likelihood (FIML) so as to avoid the use of prior
distributions. A very robust algorithm is needed to find the global maximum
within the relevant parameter space. I suggest such an algorithm and show
that it is possible to estimate the model of Smets and Wouters (2007) using
FIML. Inference is carried out using stochastic bootstrapping techniques.
Several FIML estimates turn out to be significantly diffrent from the
Bayesian estimates and the reasons behind those differences are
Keywords: Bayesian methods; Maximum likelihood; Business Cycles; Estimate DSGE models; (follow links to similar papers)
JEL-Codes: C11; E32; E32; E37; (follow links to similar papers)
51 pages, December 22, 2015
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